Robust Partial Least Squares Path Modeling

Tamara Schamberger*, Florian Schuberth, Jörg Henseler, Theo K. Dijkstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)
81 Downloads (Pure)


Outliers can seriously distort the results of statistical analyses and thus threaten the validity of structural equation models. As a remedy, this article introduces a robust variant of Partial Least Squares Path Modeling (PLS) and consistent Partial Least Squares (PLSc) called robust PLS and robust PLSc, respectively, which are robust against distortion caused by outliers. Consequently, robust PLS/PLSc allows to estimate structural models containing constructs modeled as composites and common factors even if empirical data are contaminated by outliers. A Monte Carlo simulation with various population models, sample sizes, and extents of outliers shows that robust PLS/PLSc can deal with outlier shares of up to 50% without distorting the estimates. The simulation also shows that robust PLS/PLSc should always be preferred over its traditional counterparts if the data contain outliers. To demonstrate the relevance for empirical research, robust PLSc is applied to two empirical examples drawn from the extant literature.
Original languageEnglish
Pages (from-to)307-334
Number of pages28
Early online date19 Jul 2019
Publication statusPublished - 1 Jan 2020


  • UT-Hybrid-D
  • Robust partial least squares path modeling
  • Robust correlation
  • Robust consistent partial least squares
  • Composites
  • Outliers

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